Object detectors often perform well in-distribution, yet degrade sharply on a different benchmark. We study cross-dataset object detection (CD-OD) through a lens of setting specificity. We group benchmarks into setting-agnostic datasets with diverse everyday scenes and setting-specific datasets tied to a narrow environment, and evaluate a standard detector family across all train--test pairs. This reveals a clear structure in CD-OD: transfer within the same setting type is relatively stable, while transfer across setting types drops substantially and is often asymmetric. The most severe breakdowns occur when transferring from specific sources to agnostic targets, and persist after open-label alignment, indicating that domain shift dominates in the hardest regimes. To disentangle domain shift from label mismatch, we compare closed-label transfer with an open-label protocol that maps predicted classes to the nearest target label using CLIP similarity. Open-label evaluation yields consistent but bounded gains, and many corrected cases correspond to semantic near-misses supported by the image evidence. Overall, we provide a principled characterization of CD-OD under setting specificity and practical guidance for evaluating detectors under distribution shift. Code will be released at \href{[https://github.com/Ritabrata04/cdod-icpr.git}{https://github.com/Ritabrata04/cdod-icpr}.

翻译：目标检测器通常在分布内表现良好，但在不同基准测试上性能会急剧下降。我们通过设定特异性的视角研究跨数据集目标检测（CD-OD）。我们将基准数据集划分为包含多样化日常场景的设定无关数据集和与狭窄环境绑定的设定特定数据集，并评估标准检测器系列在所有训练-测试配对上的表现。这揭示了CD-OD中存在清晰的结构：相同设定类型内的迁移相对稳定，而跨设定类型的迁移则显著下降且常呈现不对称性。最严重的性能崩溃发生在从特定源数据集向无关目标数据集的迁移中，并且这种崩溃在开放标签对齐后依然存在，表明在最具挑战性的场景中域偏移占主导地位。为分离域偏移与标签不匹配的影响，我们比较了封闭标签迁移与使用CLIP相似度将预测类别映射到最近目标标签的开放标签协议。开放标签评估带来一致但有界的性能提升，许多修正后的案例对应于图像证据支持的语义近似错误。总体而言，我们为设定特异性下的CD-OD提供了原则性表征，并为分布偏移下的检测器评估提供了实用指导。代码将在 \href{https://github.com/Ritabrata04/cdod-icpr.git}{https://github.com/Ritabrata04/cdod-icpr} 发布。